Added combinations(n,k) to RichPipe

src/main/scala/com/twitter/scalding/mathematics/Combinatorics.scala

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+package com.twitter.scalding.mathematics
+import com.twitter.scalding._
+import com.twitter.scalding.Dsl._
+import cascading.flow.FlowDef
+import cascading.tuple.{Fields, TupleEntry}
+import cascading.pipe.Pipe
+
+/**
+Serve as a repo for self-contained combinatorial functions with no dependencies
+such as
+combinations, aka n choose k, nCk
+permutations , aka nPk
+subset sum : numbers that add up to a finite sum
+weightedSum: For weights (a,b,c, ...), want integers (x,y,z,...) to satisfy constraint  |ax + by + cz + ... - result | < error
+...
+
+@author : Krishnan Raman, kraman@twitter.com
+*/
+
+object Combinatorics {
+
+/**
+  Given an int k, and an input of size n,
+  return a pipe with nCk combinations, with k columns per row
+
+
+  Computes nCk = n choose k, for large values of nCk
+
+  Use-case: Say you have 100 hashtags sitting in an array
+  You want a table with 5 hashtags per row, all possible combinations
+  If the hashtags are sitting in a string array, then
+  combinations[String]( hashtags, 5)
+  will create the 100 chose 5 combinations.
+
+  Algorithm: Use k pipes, cross pipes two at a time, filter out non-monotonic entries
+
+  eg. 10C2 = 10 choose 2
+  Use 2 pipes.
+  Pipe1 = (1,2,3,...10)
+  Pipe2 = (2,3,4....10)
+  Cross Pipe1 with Pipe2 for 10*9 = 90 tuples
+  Filter out tuples that are non-monotonic
+  For (t1,t2) we want t1<t2, otherwise reject.
+  This brings down 90 tuples to the desired 45 tuples = 10C2
+  */
+  def combinations[T](input:IndexedSeq[T], k:Int)(implicit flowDef:FlowDef):Pipe = {
+
+    // make k pipes with 1 column each
+    // pipe 1 = 1 to n
+    // pipe 2 = 2 to n
+    // pipe 3 = 3 to n etc
+    val n = input.size
+    val allc = (1 to k).toList.map( x=> Symbol("n"+x)) // all column names
+
+    val pipes = allc.zipWithIndex.map( x=> {
+        val num = x._2 + 1
+        val pipe = IterableSource( (num to n), x._1 ).read
+        (pipe, num)
+    })
+
+    val res = pipes.reduceLeft( (a,b) => {
+      val num = b._2
+      val prevname = Symbol("n" + (num - 1))
+      val myname = Symbol( "n" + num)
+      val mypipe = a._1
+                  .crossWithSmaller(b._1)
+                  .filter( prevname, myname ){
+                    foo:(Int, Int) =>
+                    val( nn1, nn2) = foo
+                    nn1 < nn2
+                  }
+      (mypipe, -1)
+    })._1
+
+    (1 to k).foldLeft(res)((a,b)=>{
+      val myname = Symbol( "n" + b)
+      val newname = Symbol("k" + b)
+      a.map(myname->newname){
+        inpc:Int => input(inpc-1)
+      }.discard(myname)
+    })
+
+  }
+
+  /**
+  Return a pipe with all nCk combinations, with k columns per row
+  */
+  def combinations(n:Int, k:Int)(implicit flowDef:FlowDef) = combinations[Int]((1 to n).toArray, k)
+
+  /**
+  Return a pipe with all nPk permutations, with k columns per row
+  For details, see combinations(...) above
+  */
+
+
+
+  def permutations[T](input:IndexedSeq[T], k:Int)(implicit flowDef:FlowDef):Pipe = {
+
+    val n = input.size
+    val allc = (1 to k).toList.map( x=> Symbol("n"+x)) // all column names
+
+    val pipes = allc.map( x=> IterableSource(1 to n, x).read)
+
+    // on a given row, we cannot have duplicate columns in a permutation
+    val res = pipes
+              .reduceLeft( (a,b) => { a.crossWithSmaller(b) })
+              .filter( allc ) {
+                 x: TupleEntry => Boolean
+                val values = (0 until allc.size).map( i=> x.getInteger( i.asInstanceOf[java.lang.Integer]))
+                values.size == values.distinct.size
+              }
+
+     // map numerals to actual data
+    (1 to k).foldLeft(res)((a,b)=>{
+      val myname = Symbol( "n" + b)
+      val newname = Symbol("k" + b)
+      a.map(myname->newname){
+        inpc:Int => input(inpc-1)
+      }.discard(myname)
+    })
+
+  }
+
+  /**
+  Return a pipe with all nPk permutations, with k columns per row
+  */
+  def permutations(n:Int, k:Int)(implicit flowDef:FlowDef) = permutations[Int]((1 to n).toArray, k)
+
+
+  /**
+  Goal: Given weights (a,b,c, ...), we seek integers (x,y,z,...) to satisft
+  the constraint  |ax + by + cz + ... - result | < error
+
+  Parameters: The weights (a,b,c,...) must be non-negative doubles.
+  Our search space is 0 to result/min(weights)
+  The returned pipe will contain integer tuples (x,y,z,...) that satisfy ax+by+cz +... = result
+
+  Note: This is NOT Simplex
+  WE use a slughtly-improved brute-force algorithm that performs well on account of parallelization.
+  Algorithm:
+      Create as many pipes as the number of weights
+      Each pipe copntains integral multiples of the weight w ie. (0,1w,2w,3w,4w,....)
+      Iterate as below -
+        Cross two pipes
+        Create a temp column that stores intermediate results
+        Apply progressive filtering on the temp column
+        Discard the temp column
+      Once all pipes are crossed, test for temp column within error bounds of result
+      Discard duplicates at end of process
+
+  Usecase: We'd like to generate all integer tuples for typical usecases like
+
+  0. How many ways can you invest $1000 in facebook, microsoft, hp ?
+  val cash = 1000.0
+  val error = 5.0 // max error $5, so its ok if we cannot invest the last $5 or less
+  val (FB, MSFT, HP) = (23.3,27.4,51.2) // share prices
+  val stocks = IndexedSeq( FB,MSFT,HP )
+  weightedSum( stocks, cash, error).write( Tsv("invest.txt"))
+
+  1. find all (x,y,z) such that 2x+3y+5z = 23, with max error 1
+  weightedSum( IndexedSeq(2.0,3.0,5.0), 23.0, 1.0)
+
+  2. find all (a,b,c,d) such that 2a+12b+12.5c+34.7d = 3490 with max error 3
+  weightedSum( IndexedSeq(2.0,12.0,2.5,34.7),3490.0,3.0)
+
+  This is at the heart of portfolio mgmt( Markowitz optimization), subset-sum, operations-research LP problems.
+
+  */
+
+  def weightedSum( weights:IndexedSeq[Double], result:Double, error:Double)(implicit flowDef:FlowDef):Pipe = {
+    val numWeights = weights.size
+    val allColumns = (1 to numWeights).map( x=> Symbol("k"+x))
+
+    // create as many single-column pipes as the number of weights
+    val pipes = allColumns.zip(weights).map( x=> {
+      val (name,wt) = x
+      IterableSource( (0.0 to result by wt), name).read
+    }).zip( allColumns )
+
+    val first = pipes.head
+    val accum = (first._1, List[Symbol](first._2))
+    val rest = pipes.tail
+
+    val res = rest.foldLeft(accum)((a,b)=>{
+
+      val (apipe, aname) = a
+      val (bpipe, bname) = b
+      val allc = (List(aname)).flatten ++ List[Symbol](bname)
+
+      // Algorithm:
+      // Cross two pipes
+      // Create a temp column that stores intermediate results
+      // Apply progressive filtering on the temp column
+      // Discard the temp column
+      // Once all pipes are crossed, test for temp column within error bounds of result
+      // Discard duplicates at end of process
+
+      ( apipe.crossWithSmaller(bpipe)
+        .map(allc->'temp){
+          x:TupleEntry =>
+          val values = (0 until allc.size).map( i=> x.getDouble( i.asInstanceOf[java.lang.Integer]))
+          values.sum
+        }.filter('temp){
+          x:Double => if( allc.size == numWeights) (math.abs(x-result)<= error) else (x <= result)
+        }.discard('temp), allc )
+    })._1.unique(allColumns)
+
+    (1 to numWeights).zip(weights).foldLeft( res) ((a,b) => {
+        val (num,wt) = b
+        val myname = Symbol("k"+num)
+        a.map( myname->myname){ x:Int => (x/wt).toInt }
+    })
+  }
+
+  /**
+  Does the exact same thing as weightedSum, but filters out tuples with a weight of 0
+  The returned pipe contain only positive non-zero weights.
+  */
+  def positiveWeightedSum( weights:IndexedSeq[Double], result:Double, error:Double)(implicit flowDef:FlowDef):Pipe = {
+    val allColumns = (1 to weights.size).map( x=> Symbol("k"+x))
+    weightedSum( weights, result, error).filter( allColumns ){
+      x:TupleEntry => (0 until allColumns.size).map( i=> x.getDouble(i.asInstanceOf[java.lang.Integer])!=0.0).reduceLeft(_&&_)
+    }
+  }
+
+
+}

src/test/scala/com/twitter/scalding/mathematics/CombinatoricsTest.scala

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+package com.twitter.scalding.mathematics
+
+import org.specs._
+import com.twitter.scalding._
+
+class CombinatoricsJob(args : Args) extends Job(args) {
+  val C = Combinatorics
+  C.permutations( 10,3 ).write(Tsv("perms.txt"))
+
+  C.combinations( 5,2 ).write(Tsv("combs.txt"))
+
+  // how many ways can you invest $10000 in KR,ABT,DLTR,MNST ?
+  val cash = 1000.0
+  val error = 1.0 // max error $1, so its ok if we cannot invest the last dollar
+  val (kr,abt,dltr,mnst) = (27.0,64.0,41.0,52.0) // share prices
+  val stocks = IndexedSeq( kr,abt,dltr,mnst)
+
+
+  C.weightedSum( stocks, cash,error).write( Tsv("invest.txt"))
+  C.positiveWeightedSum( stocks, cash,error).write( Tsv("investpos.txt"))
+
+}
+
+class CombinatoricsJobTest extends Specification {
+  noDetailedDiffs()
+  import Dsl._
+
+  "A Combinatorics Job" should {
+    JobTest( new CombinatoricsJob(_))
+      .sink[(Int,Int)](Tsv("perms.txt")) { pbuf =>
+        val psize = pbuf.toList.size
+        "correctly compute 10 permute 3 equals 720" in {
+          psize must be_==(720)
+        }
+      }
+      .sink[(Int,Int)](Tsv("combs.txt")) { buf =>
+        val csize = buf.toList.size
+        "correctly compute 5 choose 2 equals 10" in {
+          csize must be_==(10)
+        }
+      }
+      .sink[(Int,Int,Int,Int)](Tsv("invest.txt")) { buf =>
+        val isize = buf.toList.size
+        "correctly compute 169 tuples that allow you to invest $1000 among the 4 given stocks" in {
+          isize must be_==(169)
+        }
+      }
+      .sink[(Int,Int,Int,Int)](Tsv("investpos.txt")) { buf =>
+        val ipsize = buf.toList.size
+        "correctly compute 101 non-zero tuples that allow you to invest $1000 among the 4 given stocks" in {
+          ipsize must be_==(101)
+        }
+      }
+      .run
+      .finish
+  }
+}